Risk Prediction Model for Endometrial Cancer using multiple Machine Learning Algorithms and Meta-Analysis
Principal Investigator
Name
Denis Mihaies
Degrees
Undergraduate Degree in Computer Science
Institution
Brunel Univeristy
Position Title
Student
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-590
Initial CDAS Request Approval
Mar 16, 2020
Title
Risk Prediction Model for Endometrial Cancer using multiple Machine Learning Algorithms and Meta-Analysis
Summary
I am doing my final year project in computer science which involves creating a risk prediction model for endometrial cancer using different machine learning algorithms and I need a dataset which contains the risk factors associated with this type of cancer, preferably, BMI, SmokingStatus, Age, Parity, Breastfeeding, HRT use, Type 2 Diabetes, Hypertension, Contraceptive Use and the diagnosis.
Aims
The aim of this project is to create a piece of software which can assist physicians to make better decisions and help patients make an informed choice about their treatment in endometrial cancer.
The objectives:
Find an accurate percentage of risk for each individual risk factor. (That was done by the meta-analysis I concluded)
Find correlations between risk factors.
Create a model which predicts patients with endometrial cancer.
Provide personalised prevention techniques to reduce risk according to the patient’s exposure to risk factors.
Collaborators
On this project I am collaborating with my supervisor Annette Payne